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Synthesis of bulk hexagonal diamond

Physics-informed extreme learning machine (PIELM) for consolidation around an expanded cylindrical cavity

This paper proposes a physics-informed extreme learning machine (PIELM) for analyzing consolidation immediately after cavity expansion. The deep neural networks in traditional physics-informed neural network (PINN) framework are substituted by the extreme learning machine (ELM) network with only one hidden layer. By using exact definition of stress invarients, the distribution of excess water pressure after cavity expansion is rigorously incorporated into PIELM framework as initial conditions. Then, a loss vector is obtained by combining governing equation, initial conditions and boundary conditions, and the ELM network can be directly trained by optimising the loss vector via the least squares method. It is found that: (i) the PIELM approach can provide accurate prediction for consolidation analysis after cavity expansion; and (ii) the dissipation of excess water pressure heavily relies on its initial distribution that is related to soil mechanical behaviour. This proposed approach can serve as an efficient tool to interpret consolidation coefficient from piezocone penetration tests (CPTU) with measured data.

Predicting in-hospital mortality in ICU patients with Coronary heart disease and diabetes mellitus using machine learning models

Background Coronary heart disease (CHD) and diabetes mellitus are highly prevalent in intensive care units (ICUs) and significantly contribute to high in-hospital mortality rates. Traditional risk stratification models often fail to capture the complex interactions among clinical variables, limiting their ability to accurately identify high-risk patients. Machine learning (ML) models, with their capacity to analyze large datasets and identify intricate patterns, provide a promising alternative for improving mortality prediction accuracy. Objective This study aims to develop and validate machine learning models for predicting in-hospital mortality in ICU patients with CHD and diabetes, and enhance model interpretability using SHapley Additive exPlanation (SHAP) values, thereby providing a more accurate and practical tool for clinicians. Methods We conducted a retrospective cohort study using data from the MIMIC-IV database, focusing on adult ICU patients with a primary diagnosis of CHD and diabetes. We extracted baseline characteristics, laboratory parameters, and clinical outcomes. The Boruta algorithm was employed for feature selection to identify variables significantly associated with in-hospital mortality, and 16 machine learning models, including logistic regression, random forest, gradient boosting, and neural networks, were developed and compared using receiver operating characteristic (ROC) curves and area under the curve (AUC) analysis. SHAP values were used to explain variable importance and enhance model interpretability. Results Our study included 2,213 patients, of whom 345 (15.6%) experienced in-hospital mortality. The Boruta algorithm identified 29 significant risk factors, and the top 13 variables were used for developing machine learning models. The gradient boosting classifier achieved the highest AUC of 0.8532, outperforming other models. SHAP analysis highlighted age, blood urea nitrogen, and pH as the most important predictors of mortality. SHAP waterfall plots provided detailed individualized risk assessments, demonstrating the model’s ability to identify high-risk subgroups effectively. Conclusions Machine learning models, especially the gradient boosting classifier, demonstrated superior performance in predicting in-hospital mortality in ICU patients with CHD and diabetes, outperforming traditional statistical methods. These models provide valuable insights for risk stratification and have the potential to improve clinical outcomes. Future work should focus on external validation and clinical implementation to further enhance their applicability and effectiveness in managing this high-risk population.

Early cellular events of osteomucosal healing in the tooth extraction socket

Healing after dentoalveolar trauma, such as tooth extraction, is unique to the oral cavity that involves osteomucosal healing – healing of soft and hard tissues at the same time – through a series of healing stages. The healing process of soft or hard tissues is well-documented previously; however, inter-dependency and cross-talks during the progression of their simultaneous healing processes remain unclear. In this study, we investigated spatial and temporal changes of epithelial, connective, and bone tissues, as well as the presence of osteoclasts, during the early stages of osteomucosal healing. We extracted the maxillary first molars in mice and examined the osteomucosal healing process daily for 7 days using histology, immunohistochemistry, and micro-computed tomography (microCT). Epithelial tissues closed progressively throughout 7 days. Collagen deposition began in the extraction sockets as early as day 2, forming a scaffold essential for both epithelial tissue closure and bone formation. Osteoclasts appeared on day 2, steadily increasing until day 5 and remained around the socket walls but not within the sockets. Woven bone formed rapidly around day 5, with significant mineralization observed by day 7. Notably, we identified elevated expression of RANKL throughout the process and a sharp increase in OPG near new bone on day 6. These findings demonstrated the sequential and coordinated mechanisms underlying early osteomucosal healing and provide novel insights into the critical early steps required for proper healing in the oral cavity.

Spectral efficiency and BER analysis of RNN based hybrid precoding for cell free massive MIMO under terahertz communication

In future wireless networks, integrating Terahertz (THz) communication with cell-free massive multiple-input multiple-output (CFMM) systems presents a promising approach to achieving high data rates and low latency. This paper investigates the use of recurrent neural network (RNN)-based hybrid precoding in CFMM systems operating in the THz band. The proposed method jointly designs analog and digital precoders to adapt to dynamic channel conditions and user mobility. However, THz communication is challenged by high path loss and sparse scattering, which complicate accurate channel estimation. To address this, the RNN is trained to predict optimal precoding weights by learning spatial and temporal channel patterns, thereby improving channel estimation and mitigating pilot contamination. Simulation results show that the proposed method achieves higher spectral efficiency and lower bit error rate (BER) than conventional techniques. Specifically, the RNN-based approach attains a spectral efficiency of 10 bps/Hz at a signal-to-noise ratio (SNR) of 30 dB, compared to 8.2 bps/Hz for minimum mean square error (MMSE) precoding. For 16-QAM, the RNN-based method achieves a BER of 10⁻⁶ at an SNR of 11 dB, while MMSE requires 12.5 dB to reach the same BER. Overall, the RNN-based hybrid precoding consistently outperforms traditional methods across various SNR levels, antenna configurations, and user densities, underscoring its potential in next-generation THz wireless systems.

Deciphering the genetic basis of grain iron and zinc content in wheat under heat and drought stress using GWAS

Wheat, a crucial food crop, is inherently deficient in essential micronutrients such as iron and zinc. Climate change exacerbates its vulnerability to abiotic stresses like drought and heat. Developing varieties that are both climate-resilient and nutrient-dense offers a sustainable approach. The objective of this study was to identify genomic regions linked to grain Fe content (GFeC), grain Zn content (GZnC) and thousand grain weight (TGW) traits in wheat grains subjected to heat and drought stress through genome-wide association studies (GWAS). A genetically diverse set of 280 wheat genotypes was assessed across three conditions: timely sown, late sown (heat stress), and restricted irrigation (drought stress) over two years. Variation in iron and zinc levels among genotypes was significant among the conditions, with moderate heritability. Through GWAS 37 significant MTAs across the conditions were identified. For thousand grain weight (TGW) 12 MTAs, for grain Fe content (GFeC) 14 MTAs, and for grain Zn content (GZnC) 11 MTAs were detected. Notably, four MTAs for GFeC two of which were specific to heat stress were located on chromosome 7A. Among these, AX-94432820 (LSIR_23) resides near a RING-H2 finger protein gene involved in metal-ion binding. Additionally, the stable SNP AX-94953068, also on 7A, is adjacent to TraesCS7A02G171600, a gene implicated in stress response. For GZnC, the stable SNP AX-95001849 (r2 = 12.89%) was significant under both TSIR and TSRI, it maps to a plasma membrane ATPase. Using multivariate analysis, MGIDI scores were calculated, identifying nine genotypes that excelled for all three traits and conditions: RAJ4546, UP3063, HD3334, DBW296, MP1368, DBW333, UP3058, DBW332, and BRW3863. These findings will support biofortification breeding of the nutri-rich wheat varieties.

Musa Paradisiaca derived intrinsically heteroatom doped carbon dots as antioxidant and controlled drug release behavior

This work presents a green, single-step hydrothermal synthesis of intrinsically nitrogen-doped Carbon dots (MCDs) derived from Musa paradisiaca, a low-cost and renewable biomass source. The eco-friendly synthesis avoids external dopants or harsh chemicals, offering a scalable and sustainable alternative to conventional multistep methods. The resulting MCDs, with an average particle size of 4.2 nm (TEM), display desirable surface functionalities (FTIR, XPS) and heteroatom doping. Optical characterization revealed a broad UV-vis absorption at 280 nm and strong blue photoluminescence at 440 nm. DLS and zeta potential measurements confirmed excellent colloidal stability. The MCDs demonstrated high antioxidant activity (>80% radical scavenging) and biocompatibility in cellular assays. Moreover, they enabled controlled drug release, underlining their promise as multifunctional nanocarriers. Given their green synthesis, stability, and performance, these MCDs are highly suitable for future biomedical and clinical applications, particularly in antioxidant therapy and targeted drug delivery.

The father’s singing voice may impact premature infants’ brain more than their mother’s: A NICU single-arm exploratory study protocol and preliminary data on a singing and EEG framework based on the fundamental frequency of voice and kinship

This article reports the protocol of a single-arm exploratory study investigating the impact of singing on the brain activity of premature infants in the Neonatal Intensive Care Unit (NICU). The study focuses on how the differentiation of voices, as defined by the fundamental frequency (F0) shaped by biological sex and kinship, influences neurophysiological responses when measured by electroencephalography (EEG). Premature infants, who are highly sensitive to auditory stimuli, may benefit from music-based interventions; however, there is limited understanding of how voice variations between male and female caregivers, and whether they are biologically related, affect brain activity. Our protocol outlines a structured intervention where infants are exposed to singing by four facilitators – a male and a female music therapist, the mother, and the father – and includes two singing stages: a sustained note and a lullaby, both interspersed with silent periods to allow for baseline measurements. EEG recordings track brain activity throughout these sessions, followed by quantitative EEG (qEEG) analysis and thorough statistical computations (e.g., mixed-effects models, spectral power analysis, and post-hoc tests) to explore how these auditory stimuli influence brain function. Preliminary data from five infants show that maternal singing elicits the highest delta spectral power in all measured conditions except during the ‘lullaby song’, where paternal singing elicits the highest effects followed by the male music therapist and then the mother. These early findings highlight the potential influence of parental voices, particularly the fathers’ voice, on neonatal brain development, while the detailed study protocol ensures rigor and replicability, providing a robust framework for future research. (clinicaltrials.gov unique identifier: NCT06398912).

A hybrid AI model integrating BKA-VMD and deep neural networks for industrial power load prediction

Accurate power load prediction is crucial for optimizing energy consumption and enhancing efficiency in industrial environments. However, the highly nonlinear and non-stationary nature of power load time series presents significant challenges. To address this, we propose a novel hybrid deep learning model that integrates optimized data decomposition with advanced sequence modeling to enhance feature extraction and temporal pattern learning. Specifically, Variational Mode Decomposition (VMD) optimized by the Black-Winged Kite Algorithm (BKA) extracts intrinsic mode functions, reducing noise and improving signal representation. The decomposed signals are processed by a hybrid neural network combining a One-Dimensional Convolutional Neural Network (1DCNN) for local feature extraction, a Bidirectional Temporal Convolutional Network (BiTCN) for long-range temporal dependencies, a Bidirectional Gated Recurrent Unit (BiGRU) for sequential pattern learning, and an attention mechanism to emphasize critical features. Extensive experiments, including comparisons with state-of-the-art models and ablation studies, validate our approach across three diverse industrial datasets. The results demonstrate that our model significantly outperforms existing methods, achieving lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The ablation study highlights the critical roles of the attention mechanism and the BiTCN-BiGRU combination in capturing complex temporal dependencies. These findings underscore the model’s robustness and adaptability for power load forecasting. Future research should focus on enhancing generalization and validating applicability across diverse industrial settings.

Optimal design of Halbach magnetized magnetic screw for wave energy converters based on KELM network optimized by weighted mean of vectors algorithm

This paper investigates the parameter optimization problem of Halbach magnetized magnetic screw (HMMS) for wave energy converter (WECs). A magnetic screw with improved Halbach magnetized PMs arrays is presented. To further enhance the thrust density of HMMS, a HMMS optimization model is proposed based on the kernel extreme learning machine (KELM) optimized by weIght meaN oF vectOrs (INFO) algorithm. The topology and working principle of HMMS are introduced. Based on comprehensive sensitivity analysis, the design space is stratified, and the nonlinear coupling relationships between parameters are addressed using the Kriging model to enhance the accuracy and efficiency of the optimization process. The INFO algorithm optimizes kernel parameters and regularization coefficients of KELM, which critically affect its output accuracy. By establishing the INFO-KELM optimization model, the final optimized structure is obtained. The FEA is utilized to assess the properties of the HMMS. Compared with the traditional radially magnetized screw, the thrust force of the optimized HMMS increased by 40.8%. Finally, a prototype is developed and platform tests are performed to validate the theoretical analysis results.

An investigation into the statistical precision attainable with a distribution-free method of constructing age-dependent reference centiles

The distribution-free approach to the construction of age-dependent reference centiles which has been originally published by this author in 1995 and applied since then in a multitude of large-scale studies has never been investigated from a sample-size planning perspective. In the present paper, this gap is filled using the precision criterion introduced by Jennen-Steinmetz and Wellek (2005) for the estimation of reference centiles for quantitative diagnostic markers being independent of other variables, and extended by Jennen-Steinmetz (2014) to the study of age-dependent markers. In the age-dependent case, that criterion does not admit an exact representation as a function of the sample size, even when interest is in estimating a one-sided reference limit. Hence, all sample-size results presented here are based on Monte Carlo simulation. The computations cover a broad range of conditional distributions of the marker at given age including both symmetric and positively skewed distributions. For the relationship between the conditional standard deviation and age, a linear function of different slopes was assumed. Except for the most extreme settings investigated, the sample sizes shown in the tables summarizing our numerical results do not exceed the order of magnitude which has been available for a recent, potentially very influential reference-value study of basic parameters making-up the normal fetal growth profile. Furthermore, our results suggest that in terms of sample-size requirements, the distribution-free approach of Wellek & Merz (1995) to the construction of age-dependent reference ranges is typically a good bit more efficient than reference-range determination by means of quantile regression.

Future Sequon Finder - A novel approach for predicting future N-linked glycosylation sequon locations on viral surface proteins

Influenza viruses are known to evade host immune responses by shielding vulnerable surface protein epitopes via N-linked glycosylation. A program titled Future Sequon Finder was developed to predict the locations in which glycan binding sites are most likely to emerge in future influenza hemagglutinin proteins. The predictive modeling approach considers how closely sites in currently circulating strains resemble glycosylation sequons at the nucleic acid level, the surface accessibility of those sites, and the mutation frequency of amino acids at those sites that would need to change to form a glycosylation sequon. The efficacy of this model is tested using historic human H1N1 and H3N2 influenza strains along with swine H1N1 strains. Through this analysis, it is revealed that glycosylation addition events in influenza hemagglutinin proteins are typically the result of single nucleotide mutation events. It is also demonstrated that site-specific mutation frequency and surface accessibility are powerful predictors of which sites will become glycosylated in human influenza viruses when considered with the genetic composition of the sites in question. Having been designed to incorporate these factors, the program successfully predicted almost every historic sequon addition event (28/30 in human IFVs, 14/15 in swine IFVs). For human strains, it also ranked the correct near-sequons highly among falsely predicted sequons based on site-specific mutation frequency. After demonstrating the model’s power with historical data, the program was used to predict future HA glycosylation sequon locations based on currently circulating human influenza viruses.

Plantar pressures and stabilometry effects of ischemic compression in Flexor digitorum brevis muscle Myofascial Trigger Point: A prepost study

Background Ischemic compression is a manual therapy that improves range of motion, pain and disability in Myofascial Pain Syndrome. Plantar foot pain is a common clinical entity that could be due to Flexor digitorum brevis trigger point. Effect on balance and plantar pressures after ischemic compression in Flexor digitorum brevis muscle trigger point have not been checked. Methods Eighteen subjects (aged 25.06 + /- 5.51 years) with bilateral Flexor digitorum brevis latent or active myofascial trigger points were recruited. Study design: pre-post study. We measured three static footprint and stabilometry variables before and after ischemic compression for 90 seconds at bilateral Flexor digitorum brevis Myofascial Trigger Point. A Shapiro-Wilk test was performed to check normality. Comparison of related measures was done by paired T-test or Wilcoxon Range Test depending on whether the distribution was normal or non-normal. Significant differences were considered with p-value <0.05. All statistics were calculated with a 95% confidence interval. Reliability was also assessed with an Intraclass correlation coefficient (ICC) and Standard error measured (SEM) calculation. Results Most variables have good to perfect reliability, with the exception of four variables which had moderate reliability and two variables which had only slight reliability. Reliable stabylometric variables included anteroposterior displacement of COP and surface with EO and EC. The footprint and stabilometry variables showed no significant differences after ischemic compression. Conclusions Ischemic compression in the Flexor digitorum brevis muscle showed no significant differences in plantar pressures and stabilometry. Other techniques like dry needling indicated worsened balance effects. More studies are required to check significant changes. The results are important because they demonstrate a technique to treat FDB MTrP without repercussions on plantar pressure or balance. NCT06509347 (clinicalTrials.gov) initial release 7/7/24 and last release 28/7/24.

Enhanced stability of simulated leukocytes for hematology internal quality control samples: A material improvement

Objectives This study aimed to develop and evaluate simulated leukocytes derived from porcine leukocytes as a stable alternative for internal quality control (IQC) in hematology laboratories. Addressing challenges related to material stability and availability, the research contributes to improving laboratory quality assurance in Vietnam. Methods Statistical methods including Shapiro-Wilk test, Levene’s test, t-tests, ANOVA, and the IQR method were applied to assess post-production quality and establish target values. Target values were established from data collected across 90 laboratories. The Interquartile Range (IQR) method was used to eliminate outliers, and target values were set for three analyzers (Sysmex XN-1000, Sysmex XN-550, and Horiba ABX Micros 60) per batch at three concentration levels. Sample stability was evaluated over a two-month shelf life using t-tests and monitored for 20 days post-opening with repeated measures ANOVA. Results Simulated leukocytes derived from goose erythrocytes exhibited instability at high concentration levels, whereas those derived from porcine leukocytes maintained stability over two months and demonstrated acceptable performance for up to 10 days post-opening. Conclusions The findings highlight the potential of porcine leukocytes as a reliable IQC material for hematology, meeting the stability and performance requirements of clinical laboratories.

Comparison of pregnancy outcomes after history-indicated and ultrasound-indicated cervical cerclage: A systematic review and meta-analysis

Objective To compare maternal and neonatal outcomes in women with a previous history of pregnancy loss and/or preterm delivery who underwent ultrasound-indicated cerclage (UIC) or history-indicated cerclage (HIC). Methods PubMed, Web of Science, Scopus, and Embase databases were searched for observational studies and randomized controlled trials (RCT) from inception to 30 April 2024. Eligible studies should have compared the outcomes of women with singleton pregnancies who underwent UIC or HIC. STATA version 15.0 was employed, and the analysis was done using a random effects model and unadjusted effect sizes from the included studies. Results Of 25 included studies (n = 3909), most (n = 18) were retrospective cohort studies. Compared to women who underwent HIC, UIC was associated with higher risk of having a preterm birth (<37 weeks of gestation) (OR 1.48, 95% CI: 1.17, 1.88; N = 15), low birth weight (<2500g) (OR 1.78, 95% CI: 1.32, 2.41; N = 6) and admission to neonatal intensive care unit (OR 1.70, 95% CI: 1.27, 2.27; N = 6,). Women with UIC also had a higher risk of chorioamnionitis (OR 2.34, 95% CI: 1.36, 4.04; N = 4). The risk of having a low APGAR score (5-minute score of less than 7), fetal death and preterm premature rupture of membrane (PPROM) was comparable among the two groups. Conclusion Our results demonstrate that UIC is associated with higher risks of adverse pregnancy outcomes compared to HIC. However, our evidence emanates from observational studies and is prone to biases, particularly because the findings were unadjusted for potential confounders. More clinical trials are needed to confirm our observations. Systematic review protocol registration PROSPERO CRD42024544181

Pharmaceutical expenditure changes under the volume-based procurement policy: Effects and influencing factors

Objectives To estimate the impact of China’s volume-based procurement (VBP) policy on the expenditure of both policy-covered and uncovered drugs, and to identify the elements that contribute to drug expenditure changes under VBP policy. Methods Using national drug procurement data of public medical institutions, this study included 25 policy-covered VBP drugs and 99 policy-uncovered alternative drugs as samples, seven “4+7” pilot cities and eight “4+7” expansion provinces as observation regions. Time-varying difference-in-difference (DID) model was applied to quantify policy impact on drug expenditures. The drug expenditure index decomposition method was employed to analyze the determinants of drug expenditure changes following VBP policy. Results The expenditure of VBP drugs significantly decreased by 42.19% after VBP policy (β = −0.55, p < 0.001), while alternative drugs increased by 11.52% (β = −0.11, p < 0.001), with a significant reduction in the overall expenditure of observed drugs (β = −0.05, p < 0.001). The decrease of VBP drug expenditures showed a trend of tertiary hospital (β = −0.64, p < 0.001) > secondary hospital (β = −0.57, p < 0.001) > primary healthcare centers (β = −0.39, p < 0.001). The index decomposition showed that manufacturer structure index (IM) decline was the primary driver for expenditure reduction of policy-covered drugs, with the IM decrease of 54.17% in pilot cities and 40.86% in expansion regions. The secondary driver was the price index (IP), with a decline of 31.68% in pilot cities and 36.08% in expansion regions. The restraining factor was the quantity index (IQ), increasing by 92.54% in pilot cities and 52.04% in expansion regions. IQ also drove the increase in alternative drug expenditures, increasing by 95.56% in pilot cities and 32.76% in expansion regions. Conclusion VBP policy effectively promoted the decline of total drug expenditures, primarily through manufacturer-level market displacement and the absolute price reduction. However, the “spillover effect” of alternative drugs weakened the overall effect on cost control. Strengthening holistic governance and improving the quality and intensiveness of drug use are important directions for future policy perfection.

Feasibility study for dose calculation with a radiation treatment planning system using a fixed-size electron cone applicator for small electron fields

Objective This study aims to evaluate the feasibility of radiation treatment planning using a commercial treatment planning system (TPS) for small fixed-size electron cone electron applicators not natively supported by the TPS. Methods Dosimetric characteristics, including beam profiles and output factors (OFs), were compared between a 6 MeV electron beam collimated by a small fixed-size electron cone applicator and a cerrobend cutout-based general applicator. Measurements were performed using a micro-diamond detector in a water phantom for field sizes of 2, 3, 4, and 5 cm. The monitor units (MUs) from the TPS were compared with direct measurements. To estimate the MU for the fixed-size electron cone applicator using the TPS, the relative OFs were defined as the ratio of the OFs for the fixed-size electron cone and general applicators. Dose distributions obtained from the TPS were validated against measurements using Gafchromic films, ensuring accuracy. Results Gamma analysis showed a passing rate >95% with 1%/1 mm criteria for depth dose comparisons and >99% with 2%/2 mm criteria for beam profiles. The general applicator’s OFs were consistently higher across all measured field sizes. The MU difference between the TPS and measurements was within 2.0%, while the difference between indirect TPS calculations and direct measurements for the fixed-size electron cone applicator remained within 1.0%. Dose distribution analysis showed >99% agreement (3%/3 mm) between the 2D dose distribution obtained using film in the fixed-size electron cone applicator and that calculated by the TPS of cerrobend cutout-based applicator. Conclusion The results demonstrate the feasibility of calculating monitor units and dose distributions for small fixed-size electron cone applicators using a commercial TPS combined with relative output factors. This approach offers a reliable method for dose calculation in specialized electron therapy applications.

Geospatial assessment of household water, sanitation and hygiene conditions and associated factors in Nigeria: A causal relationship model

Lack of adequate access to safe water, sanitation, and hygiene (WASH) has contributed to increased under-five mortality and morbidity of school-age children in low- and middle-income countries. Despite the global and national intervention programs, access to safe WASH remains a critical challenge in Nigeria. This study employed spatial and non-spatial statistics to establish causal relationships between WASH conditions and household factors in Nigeria. Results show that a large proportion of Nigerian households were still associated with unimproved hygiene (88%), sanitation (47%) and water (25%). Wealth status, literacy level and residency type exhibit significant causal relationships with households’ water sources (α = 0.000). Wealth status and the gender of household head exhibit significant causal relationships with sanitation condition (α = 0.000) and hygiene condition (α = 0.004 and α = 0.345, respectively). However, the computed parameter Degree of Dependence (DoD_j) shows that the choice of water sources mostly depends on residency type (DoD_j = 0.998) compared with the level of education and wealth status (DoD_j = 0.535 and 0.485, respectively). Statistical indices show that the implemented regression models are reliable (with models’ DoD of 0.714–0.996, Adjusted R2 of 0.184–0.762 and Akaike Information Criterion (AICc) of 68–103). The study concludes that a high risk of unimproved WASH is associated with rural residence, which is usually characterised by a low level of education, poverty and large household size. It further concludes that the high prevalence of unimproved hygiene, irrespective of the household wealth status and educational level, suggests the need for proper health and hygiene education. This study suggests the need for a more focused policy action towards empowering rural and vulnerable households in Nigeria with relevant preventive environmental and health information and appropriate social support for the communities.

The colonial response to the development of disease in Ghana and Côte d’Ivoire (ca. 1900-1955): A comparative analysis of British and French colonial health policies

Using a newly constructed dataset of official morbidity figures based on colonial medical reports, this article studies the British and French colonial response to the development of fourteen selected diseases in colonial health care facilities in Ghana and Côte d’Ivoire from circa 1900–1955. Yaws and malaria are shown to have received colonial attention due to their relatively high incidence in the facilities, while other diseases were deemed important for reasons other than the number of cases treated (sleeping sickness, yellow fever, smallpox). Despite similar forces surrounding colonial decision-making (such as the expansion of the colonial health care networks, population growth and the development of Western medicine), the British and French colonial response developed differently for part of the selected diseases (including sleeping sickness, measles and dysentery). For five commonly prevalent diseases (leprosy, dysentery, measles, gonorrhoea and syphilis) in Ghana and Côte d’Ivoire, the results in this article suggest that as of the 1930s, French colonial policymakers recognised their threat, while the British failed to do so sufficiently. A second new dataset of colonial vaccination campaigns (for ca. 1900–1955) formed the basis of an analysis of this aspect of the colonial response outside health care facilities. It finds that several diseases (including yellow fever) were addressed – more so in Côte d’Ivoire than in Ghana – but that smallpox vaccination campaigns trumped all others. The findings of this analysis indicate that extensive smallpox campaigns occurred earlier in both countries than previously suggested by the literature, and that the French colonial administration imposed a more elaborate programme.

Decentralised trials for hearing and tinnitus therapies: Lessons from the Digital thErapy For Improved tiNnitus carE (DEFINE) randomised controlled trial

Randomised Controlled Trials (RCTs) are the gold standard for evaluating the efficacy of interventions; yet, traditional methods involving multiple recruitment sites often involve significant logistical and financial challenges. The DEFINE trial demonstrates the feasibility of a decentralised approach to RCTs by comparing smartphone-delivered self-guided tinnitus therapy against one-to-one therapist-facilitated treatment for tinnitus. This trial was conducted entirely remotely, leveraging digital technologies for remote recruitment, data collection, and intervention delivery. A total of 210 participants were recruited through social media platforms over a five-month period. Participants were screened and enrolled by a central trial team remotely, who utilised hearing test smartphone applications and electronic consent forms. Baseline and follow-up assessments were conducted using electronic data capture (EDC) platforms, with high retention rates observed at each time point. The trial successfully recruited and retained participants, demonstrating the efficiency and cost-effectiveness of remotely managed trials. Key findings include a high engagement rate from social media ads, with 151,978 impressions leading to 4,997 clicks (3.3%), with a direct advertising spend of £880. 912 individuals self-screened for eligibility online. The median age of participants was 58.3 years, in line with comparable traditionally-recruiting tinnitus studies, with good geographical distribution across the UK. The trial’s adaptability allowed for protocol adjustments, and real-time monitoring of data quality and completeness. The DEFINE trial demonstrates that decentralised RCTs can offer a viable alternative to traditional RCTs for some hearing and tinnitus research, potentially increasing participant diversity and reducing the burden of research on participants, while maintaining rigorous standards of data collection and participant safety. Increasing clinical use of remote audiological assessment, and hearing implant programming provide increasing opportunities for the adoption of entirely remote or hybrid studies in hearing and tinnitus conditions.